There are various methods of calculating the binocular disparity between two images resulting from stereo matching. Examples of existing methods of calculating binocular disparity include a scan-line smoothness optimization method and a dynamic programming stereo (DPS) method which are implemented using ASIC and perform optimization of one-dimension energy.
However, these methods have disadvantages in that streak noise occurs in binocular disparity images due to errors occurring in scan line units of the images, and the degree of the noise depends on the arrangement condition of binocular cameras.
FIG. 1 is a view showing a binocular disparity image with streaks according to the related art, and FIG. 2 is a view showing an 8-by-8-sized block of the binocular disparity image shown in FIG. 1, in which many streaks are present.
Streaks occurring in a binocular disparity image obtained by using optimization of one-dimension energy have features in which they are formed in a horizontal direction, as shown in FIG. 1, and occur more frequently in the boundary of an object than in the central part of the object, as shown in FIG. 2.
FIG. 3 is a view showing a result image obtained by performing Fourier transform on the binocular disparity image shown in FIG. 1.
Referring to FIG. 3, if Fourier transform is performed on an image having horizontal streaks and the transformed image is observed in log scale, a vertical component strongly appears. However, since the length of the individual streaks are not long as shown in FIGS. 1 and 2, many horizontal high-frequency components also appear.
Existing techniques of removing such streaks in an image detect the streaks appearing as straight lines in a Fourier domain and remove the detected streaks by using linear regression analysis.
However, these existing techniques are limited to removing of streaks crossing an entire original image. Since short streaks locally present in an image have a great many of frequency components along the progress direction of the streaks as shown in FIG. 4, the streaks occupy a very wide range in a Fourier domain as shown in FIG. 5. Therefore, the short streaks locally present in the image cannot be removed by the linear regression analysis used by the existing techniques.
In other existing techniques of reducing streaks crossing an entire screen, since many calculations are required for a substantial number of pixels in images are required, the process speed is low and short streaks locally present in images also cannot be removed.